The following Addenda and some Corrigenda were derived for the most part from manuscripts examined in the summer of 1964 and in the spring and summer of 1965, 1967 at Bologna University ; London, British Museum ; Munich, Bayerische Staats-Bibliothek, Codex Latinus Monacensis ; Orlèans; Oxford, the Bodleian ; and Merton College ; Paris, Bibliotheque nationale , and University ; Tours; Vatican, Bibliotheca Apostolica Vaticana ; and Vienna, National-Bibliothek . A number are noted from other sources, namely Silvestre , Wickersheimer (...) , et alia. They supplement the late Lynn Thorndike's “Additional Addenda et Corrigenda” in Speculum , and are here offered for the convenience of users of the Catalogue of Incipits. jQuery.click { event.preventDefault(); }). (shrink)

The present study examines the fortuna in the Middle Ages of the Galenic work in fourteen books on Therapeutics, the De Methodo medendi or Therapeutica, known also in the medieval centuries under the titles Ars magna, Megategni and De ingenio sanitatis. After tracing the history of the translations of this text into Latin from the Arabic and the Greek, the study focuses on the De Methodo medendi's place in the medical curricula of the medieval universities. The essay closes with an (...) extensive listing of the medieval manuscript copies of this valuable Galenic treatise. (shrink)

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal (...) connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at (...) a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causality was not just a victory for common sense, but a revolution in the study of the world. (shrink)

This note aims to assist applied econometricians in understanding the tools of causal inference and to extend those discussed in Nick Huntington-Klein's review of The Book of Why.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...) models. (shrink)

We support Jaswal & Akhtar's interrogation of social motivational accounts of autism and discuss two sources of bias that contribute to how others construe autistic people's communications: an experience-based bias that limits our ability to discern the speaker's action as communicative and a prejudice against the credibility of certain speakers that limits a listener's willingness to believe their testimony.

This paper provides empirical interpretation of the do(x)do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x)do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally (...) eligible to receive the do(x)do(x) operator and to produce useful information for decision makers. (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature. (...) 1. Introduction2. Causal models and the definition of actual causality: a review2.1Causal models2.2Syntax and semantics2.3The definition of cause3. Explanation: the basic definition4. Partial explanations and explanatory power5. The general definition6. DiscussionAppendix: the formal definition of causality. (shrink)

Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest (...) in causation will also profit from reading Pearl’s book, especially the material in chapters 7, 9, and 10, which is self-contained and less technical than other parts of the book. The present review is aimed primarily at readers of the second type. (shrink)

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process (...) is due to the circadian clock. Finally, we discuss the “kicked cell cycle” model and its implication on the study of the cell cycle in healthy and cancerous tissues. (shrink)

In _A Question of Time_, Joel Pearl offers a new reading of the foundations of psychoanalytic thought, indicating the presence of an essential lacuna that has been integral to psychoanalysis since its inception. Pearl returns to the moment in which psychoanalysis was born, demonstrating how Freud had overlooked one of the most principal issues pertinent to his method: the question of time. The book shows that it is no coincidence that Freud had never methodically and thoroughly discussed time (...) and that the metaphysical assumption of linear time lies at the very heart of Freudian psychoanalysis. Pearl’s critical reading of Freud develops through an original dialogue that he creates with the philosophy of Martin Heidegger and, specifically, with the German philosopher’s notion of temporality. Pearl traces the encounter between Freud and Heidegger by observing the common inspiration shaping their thinking: philosopher Franz Brentano, who taught both Freud and Edmund Husserl, Heidegger’s mentor. The book travels down an alternate path, one overlooked by Freudian thought – a path leading from Brentano, through Husserl and onto Heidegger’s notion of time, which is founded on the ecstatic’ interrelation of past, present and future. (shrink)

I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis (...) of fitting and interpreting, guided by the Logic of Causation. (shrink)

A key motivation for Universal Grammar (UG) is developmental: UG can help children acquire the linguistic knowledge that they do as quickly as they do from the data that's available to them. Some of the most fruitful recent work in language acquisition has combined ideas about different hypothesis space building blocks with domain‐general statistical learning. Statistical learning can then provide a way to help navigate the hypothesis space in order to converge on the correct hypothesis. Reinforcement learning is a principled (...) way to update the probability of a categorical option that's in competition with other categorical options. There are two prominent options for linking theory theoretical representations: the Uniformity of Theta Assignment Hypothesis (UTAH) and the relativized form of that theory, relativized UTAH. The learning strategy of Pearl and Sprouse shares the intuition with subjacency that there's a local structural anomaly when syntactic islands occur. (shrink)

Humans routinely transmit and interpret subtle information about their mental states through the language they use, even when only the language text is available. This suggests humans can utilize the linguistic signature of a mental state, comprised of features in the text. Once the relevant features are identified, mindprints can be used to automatically identify mental states communicated via language. We focus on the mindprints of eight mental states resulting from intentions, attitudes, and emotions, and present a mindprint-based machine learning (...) technique to automatically identify these mental states in realistic language data. By using linguistic features that leverage available semantic, syntactic, and valence information, our approach achieves near-human performance on average and even exceeds human performance on occasion. Given this, we believe mindprints could be very valuable for intelligent systems interacting linguistically with humans. Keywords: mental state; linguistic features; mindprint; natural language processing; information extraction. (shrink)

Heinrich Popitz’s Phenomena of Power aims to uncover power as “a universal component in the genesis and operation of human societies”. In order to uncover this “universal” concept of power, Popitz employs Husserl’s method of the “imaginative variation” [Phantasievariation]. Yet, contrary to phenomenology’s traditionally descriptive posture, Phenomena of Power’s project is at once descriptive and normative—seeking not only to describe power, but to also describe the way in which power can be remade. In the present paper it is argued that (...) this normative component of Popitz’s project offers the burgeoning field of critical phenomenology an illustration of the way in which Husserl’s “imaginative variation” might be employed not only as a descriptive tool of pure essences, but also as an instrument in the refashioning of social reality. (shrink)

The Argonauts combines high theory and the everyday. It does this by combining lofty thought, the quotidian, close attention to words and ideas and stray thoughts, and desire. It does this through form, the way it blends and refuses genre, the way it skips from one thought or story to another, and making them connect by virtue of contiguity. The Argonauts refuses form in a way that parallels how Maggie's and Harry's bodies and identities refuse gender taxonomy. It also refuses (...) genre, referred to variously as theory, memoir, and autotheory. While it queers form and family, it also queers feelings. (shrink)